Support vector machine based nonlinear model multi-step-ahead optimizing predictive control  

作　　者：钟伟民 皮道映 孙优贤 

机构地区：[1]National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China

出　　处：《Journal of Central South University of Technology》2005年第5期591-595,共5页中南工业大学学报（英文版）

基　　金：Project(2002CB312200)supportedbytheNationalKeyFundamentalResearchandDevelopmentProgramofChina

摘　　要：A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.

关 键 词：nonlinear model predictive control support vector machine nonlinear system identification kernel function nonlinear optimization 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]